Universal strategy of dynamic subpopulation number management in practical network optimization problems

Recently, an idea of evolutionary multi-tasking has been proposed and applied to various types of optimization problems. The basic idea of evolutionary multi-tasking is to simultaneously solve multiple optimization problems (i.e., tasks) in a cooperative manner by a single run of an evolutionary algorithm. For this purpose, each individual in a population has its own task. This means that a population of individuals can be viewed as being divided into multiple sub-populations. The number of sub-populations is the same as the number of tasks to be solved. In this paper, first we explain that a multi-factorial evolutionary algorithm (MFEA), which is a representative algorithm of evolutionary multi-tasking, can be viewed as a special island model. MFEA has the following two features: (i) Crossover is performed not only within an island but also between islands, and (ii) no migration is performed between islands. Information of individuals in one island is transferred to another island through inter-island crossover. Next, we propose a simple implementation of evolutionary multi-tasking in the framework of the standard island model. Then, we compare our island model with MFEA through computational experiments. Promising results are obtained by our implementation of evolutionary multi-tasking.

The primary focus of this study is to investigate the control strategies of a hybrid system used in hydraulic excavators. First, the structure and evaluation target of hybrid hydraulic excavators are analyzed. Then the dynamic system model including batteries, motor and engine is built as the simulation environment to obtain control results. A so-called multi-work-point dynamic control strategy, which has both closed-loop speed PI (proportion integral) control and direct torque control, is proposed and studied in the simulation model. Simulation results indicate that the hybrid system with this strategy can meet the power demand and achieve better system stability and higher fuel efficiency.

Quadratic Assignment Problem (QAP) is one of the most difficult combinatorial problems in the literature and has a diverse field of applications. This paper presents the results of experiments on the impact of parallelization of a sequential GA using island model. Both of the genetic algorithms are applied to the QAP. For the island model parallel GA, we systematically change the number of islands and investigate the effects of dividing the same global population into a number of subpopulations. The number of islands is gradually increased to observe the effects on solution quality and speedup in total execution time using different problem instances. The results clearly indicate that, while parallelized version outperforms sequential counterpart in both solution quality and total execution time, an increasing number of subpopulations also positively effects the results until a critical point where every subpopulation has a certain number of individuals to be able to evolve independently. Beyond that point, the performance of the algorithm begins to decrease.

The genetic differentiation of populations is usually studied by using the equilibrium theory of Wright's infinite island model. In practice, however, populations are not always in equilibrium, and the number of subpopulations is often very small. To get some insight into the dynamics of genetic differentiation of these populations, numerical computations are conducted about the expected gene diversities within and between subpopulations by using the finite island model. It is shown that the equilibrium values of gene diversities (HS and HT) and the coefficient of genetic differentiation (GST) depend on the pattern of population subdivision as well as on migration and that the GST value is always smaller than that for the infinite island model. When the number of migrants per subpopulation per generation is greater than 1, the equilibrium values of HS and HT are close to those for panmictic populations, as noted by previous authors. However, the values of HS, HT, and GST in transient populations depend on the pattern of population subdivision, and it may take a long time for them to reach the 95 per cent range of the equilibrium values. The implications of the results obtained for the conservation of genetic variability in small populations are discussed. It is argued that any single principle should not be imposed as a general guideline for the management of small populations.

Governance for complex problem solving has been increasingly discussed in environmental sustainability research. Above all, researchers continuously observe that sustainability problems are complex or “wicked”, and suggest participatory models to address these problems in practice. In order to add to this debate, this study suggests a more differentiated theoretical approach to define governance for complex environmental problem solving than in previous studies. The approach consists of two vital steps: First, we operationalize complexity and define management strategies for solving environmental sustainability problems based on findings from psychology research. Second, we identify governance strategies that facilitate these management strategies. Linking those strategies suggests that the role of diverse institutions, actors, and interactions differs for five key dimensions of complexity: goals, variables, dynamics, interconnections, and informational uncertainty. The results strengthen systematic analyses of environmental sustainability problems in both theory and practice.

In the hot strip rolling process, the performance of a monitoring system for automatic gauge control (MN-AGC) is influenced greatly by the model mismatch which is caused by the variation of model parameters values. A constrained dynamic matrix control (CDMC) strategy that includes a prediction model, rolling optimization, and feedback correction was used in the MN-AGC. First, the conventional Smith prediction-based control strategy for the MN-AGC was analyzed. Second, the performance index function and optimal control of the CDMC strategy were determined. Finally, simulations and industrial experiments were conducted. The results showed that both control strategies provided good control performance. When model mismatch occurred, the Smith predictor-based MN-AGC resulted in significant overshoot or even oscillations but the control performance of the CDMC-based MN-AGC was not influenced by changes in the model parameters.

The high-speed rotor electric drive system in control moment gyroscopes (CMGs) is essential for precise spacecraft attitude control. Rigorous testing of this system is critical for ensuring reliability and longevity throughout orbital missions. However, conventional test bench methods exhibit numerous limitations. In contrast, the electric motor emulator (EME) provides a flexible and efficient alternative for power-level testing of the CMG high-speed rotor brushless DC motor drive system. To address the challenges of trapezoidal back-electromotive force (back-EMF) emulation and insufficient square-wave current tracking accuracy in existing brushless DC motor emulator (BLDCME) implementations, this paper proposes a sliding time window-based dynamic current compensation control (STW-DCCC) strategy for the CMG high-speed rotor BLDCME. First, based on the VSC single-conversion-circuit topology, the BLDCME basic control strategy based on the motor port current and the current change rate is implemented to achieve a tracking control of the square-wave current and emulation of the trapezoidal back-EMF. Building upon this foundation, a sliding time window-based RMS current compensation optimization strategy for the BLDCME is designed to provide dynamic compensation for system disturbances and thereby enhance the tracking accuracy of the square-wave current. Furthermore, by incorporating fault information, the proposed STW-DCCC strategy can also emulate the resistance unbalance fault of the brushless DC motor. Finally, through experiments, a comparative analysis is conducted between the basic control strategy and the proposed STW-DCCC strategy under normal operating conditions, parameter mismatch operating conditions, and resistance unbalance fault conditions, thereby validating the effectiveness of the proposed method.

With high penetration of solar Photovoltaic (PV) resources, controlling voltage of distribution systems with conventional control strategies such as fixed set-point operation mode of Load Tap Changers (LTCs) or even in some cases with Line-Drop Compensation (LDC) operation mode is not possible anymore. In case of LDC operation mode, optimizing the R & X settings is time-consuming and is required with every change in PV penetration level. Hence, a robust and dynamic control strategy is required to handle the challenges induced by high penetration levels of solar PV resources. In this work, dynamic control of LTC and Line Voltage Regulators (LVRs) of a Hawaii substation with multiple voltage control zones is implemented in OpenDSS and controlled by Python through COM interface. Then its performance in terms of reducing voltage volatility, Operation and Maintenance (O&M) costs and ramp-rate, facilitating LDC R&X settings optimization and energy saving is evaluated. Furthermore, the impact of Dynamic Var Controllers (DVCs) in accomplishing the aforementioned objectives is illustrated through comprehensive time-series simulations. Simulation results show that by using dynamic control of voltage regulators and DVCs, PV penetration increases by 45% due to reducing voltage volatility. Moreover, generation ramp-rate reduces by 8%. Furthermore, energy savings are tripled compared to energy savings achieved by existing controls. Finally, the utilization of DVCs reduces the O&M costs by 14%.

In order to extend the service life of the long-chain gear transmission system of a drum shearer, an electro-mechanical coupling model of a drum shearer cutting unit is established. The model considers the dynamic characteristics of the motor, time-varying meshing stiffness, as well as the drum load characteristics. Additionally, the dynamic characteristics and control strategy for suppressing the dynamic load of the gear transmission system under impact load are investigated based on this model. Firstly, the influence of the gear transmission system of the drum shearer cutting unit under impact load is analyzed. Then, on that basis, the active control strategy based on motor torque compensation is proposed to suppress the dynamic load of the gear transmission system caused by mutational external load. Finally, the suppression effect on the dynamic load of the gear transmission system is analyzed. Research results indicate that this control strategy has good control effects to suppress the dynamic load caused by a mutational external load, which confirms the effectiveness of the proposed control strategy.

A reasonable and effective control strategy for HEV (Hybrid Electric Vehicle) with HESS (Hybrid Energy Storage System) can improve the system efficiency and battery service life. A dynamic programming-based global optimal control strategy which fully considered the efficiency of each component in HEV is presented. To compare with the nonlinear proportion factor dynamic coordination strategy, the fuel consumption is not increased. The cycle numbers of battery are further decreased which benefits its cycle life improvement. The regenerative energy callback ratio improves 3% under the shanghai drive cycle as an example. Since this optimization algorithm considers efficiency of each subsystem, the efficiency optimum under the selected driving cycle was realized. It can also provide a reference for the non-linear scaling factor allocation strategies to determine the scale factors.

Sensitive cells: enabling tools for static and dynamic control of microbial metabolic pathways

With the increasing integration of distributed generators (DGs), renewable energy sources (RESs), and energy storage systems (ESSs) in the hybrid AC/DC microgrid (HMG), the paralleled bidirectional interlinking converters (BILCs) play a significant role in the economic power interaction (EPI) between AC and DC subgrids. This paper proposes a distributed dynamic event-triggered control (ETC) strategy for the HMG to realize the global economic operation. In each subgrid, economic power sharing (EPS) among DGs can be achieved by economic droop controllers with no global information. The proposed distributed dynamic ETC includes a continuous-time part and a discrete-time part, where the former part realizes the EPI between two subgrids, while the latter part ensures the proportional power sharing between BILCs. Thus, a particular over-stressed BILC can be avoided, and system reliability and scalability are significantly improved. Additionally, only data from neighboring BILCs at the event-triggered times are involved, which greatly minimizes the communication burden and costamong BILCs. Furthermore, the asymptotic stability of the closed-loop system dynamics and the communication time delay stability are proven. Finally, the hardware-in-loop experimental results are conducted to demonstrate the effectiveness of the proposed control strategy.

Containment control is one of critical issues in multiagent systems (MASs). A recursive sliding-mode dynamic surface control (DSC) strategy with a nonlinear gain is addressed for the containment problem of uncertain MASs in strict-feedback form. Two factors, a recursive dynamic sliding-mode surface and a nonlinear gain function, are introduced to solve the contradiction between control accuracy and dynamic performance in the traditional DSC method. The function approximation technique, by radial basis function neural networks, is employed to compensate unknown dynamics. On the basis of the backstepping and graph theory, a full-state feedback containment control strategy is presented. Subsequently, we propose an output feedback containment control strategy via a state observer for individual followers. From the Lyapunov stability theorem, we construct an improvement Lyapunov function for the MAS, and it is proven that all followers converge to a convex hull formed by leaders for both full-state feedback and output feedback cases. Simulation examples are presented for verification of the effectiveness of the strategy.

<strong>[Purposes]</strong> To investigate the impact of bus lane reuse on the operational efficiency of mixed traffic flows under an intelligent connected environment, and propose control strategies to enhance overall traffic flow efficiency. <strong>[Methods]</strong> This study analyzed the mixed traffic flows composed of connected automated vehicles (connected and automated vehicles, CAV), human-driven vehicles (human-driven vehicles, HDV), and buses, focusing on headway distances under different car-following models. A mathematical model for vehicle spatial distribution was established. Key parameters such as the optimal number of bus lanes, their setup conditions, and the optimal allocation ratio of CAV traffic were proposed, aiming at maximizing the traffic flow rate. Case studies were conducted to compare traffic flow performance under different control strategies. <strong>[Results]</strong> When traffic demand is low, bus lanes are unnecessary across all CAV penetration rates. However, as traffic demand increases, the moderate allocation of bus lanes becomes more critical, though diminishing returns are observed. In particular, excessive bus lane allocation under certain conditions may lead to congestion, hindering the improvement of traffic flow efficiency. <strong>[Conclusions]</strong> The proposed control strategy for the joint utilization of CAVs and bus lanes improves the safety and stability of mixed traffic flows and provides valuable theoretical and practical guidance for further optimization in intelligent connected transportation.

Since no engine clutch exists in the power-split system of the hybrid electric bus, the torque variations of the power sources (i.e., the engine, motor, and generator) are easy to cause system shock during mode switching. To yield a better control effect, the model predictive control (MPC) based dynamic coordination control strategy (DCCS) is considered as an effective solution to guarantee the drivability and reliability of the system. However, the existing MPC-based DCCS still have the challenge of model accuracy and computational complexity. Therefore, to solve the problem, a data-driven Fast-MPC(FMPC) based DCCS is proposed for a power-split hybrid electric bus in this paper. Firstly, to ensure the accuracy of the engine dynamic model, a piecewise data fitting method is proposed by analyzing the historical data of the engine. Then, considering the high dimension of the control model (based on the dynamics model of the power-split system), a fast solving method for the quadratic optimization problem of MPC is proposed with respect to the proper assumption and simplification. Finally, to verify the effectiveness of the proposed control strategy, offline simulation and hardware-in-the-loop (HIL) simulation experiments are carried out. Simulation results show that good driving comfort and real-time performance have been achieved. This research is expected to promote the application of MPC in the DCCS and further improve the dynamic performance of the power-split system.